Method and apparatus for processing semiconductor

ABSTRACT

In a semiconductor processing apparatus including a process chamber, a sample stand for holding a sample in the process chamber, and a process gas supply unit for supplying a process gas to the process chamber, a plurality of samples of a lot are successively supplied to a process chamber to be successively processed in an intra-lot successive process. The apparatus includes a state sensor for detecting a state in the process chamber and an intra-lot variation pattern prediction unit for predicting, according to sensor data detected by the state sensor, intra-lot variation patterns of results of the intra-lot successive process. According to a result of the prediction by the intra-lot variation pattern prediction unit, the apparatus changes a process condition applied to a sample of the lot.

BACKGROUND OF THE INVENTION

[0001] The present invention relates to a method and an apparatus forprocessing a semiconductor, and in particular, to a method and anapparatus for successively processing a semiconductor.

[0002] Semiconductor devices are produced in fine dimensions these days,and hence higher dimensional precision is required in the machining ofthe semiconductor devices. A semiconductor processing apparatus conductsphysico-chemical machining on a semiconductor wafer using heat and/orplasma. In such a processing apparatus, a product resultant fromchemical reaction in the apparatus piles as a residual product on aninner wall of the apparatus. The residual product increases with a lapseof time, and a state to process wafers gradually changes as a result.

[0003] That is, while the operation to process the wafer is repeatedlyexecuted, the state of machining the semiconductor device changes andperformance of the semiconductor device is resultantly deteriorated.

[0004] To solve the-problem, the product piled on the inner wall of aprocessing chamber is cleaned ordinarily using plasma. However, theplasma cleaning cannot completely remove the product piled on the innerwall of the processing chamber. Therefore, as described above, while theoperation to process the wafer is repeatedly executed, thickness of alayer of the product piled on a part of the inner wall of the chambergradually increases and hence a contour of machining the semiconductordevice gradually changes. Therefore, before the change in the machiningcontour becomes a problem, constituent components or parts of thesemiconductor processing apparatus are replaced or cleaned in ordinarycases.

[0005] In addition to the pile of the product, variations in variousstates of the processing apparatus also contribute to the variation inthe machining contour of the wafer. To cope with this problem, attemptshave been conducted. For example, a plasma processing apparatus senses achange in an internal state thereof and feeds a result of the sensingoperation to a process condition of the plasma processing apparatus tokeep a process characteristic at a fixed level.

[0006] For example, JP-A-6-132251 (article 1) describes an etchprocessing apparatus or an etching apparatus which monitors a result ofetching for each wafer to determine whether or not the monitored resultsatisfies an inspection condition. If the result does not satisfy thecondition, the etching apparatus modifies the process condition torestore the process characteristic.

[0007] JP-A-10-125660 (article 2) describes a plasma processingapparatus which measures an electric signal reflecting a state of plasmawhile processing a sample. According to a value of the signal thusmeasured, the apparatus calculates a predicted value of a plasma processcharacteristic and diagnoses the plasma state according to the predictedvalue.

[0008] JP-A-2001-267232 (article 3) describes a method in whichmonitored data of results of wafer process and process states in thepast are stored in a database. According to monitored data of the wafer,a result of process of a wafer at present is predicted to conductoptimization control for the process.

SUMMARY OF THE INVENTION

[0009] However, in the methods of articles 1 to 3 described above, aprocess state of a particular wafer is monitored to predict variation ina result of wafer machining operation according to data thus monitoredto control the process condition of a subsequent wafer using thepredicted variation. In consequence, when the machining result of theparticular wafer used for the prediction is not equal to that of thesubsequent wafer, the methods cannot optimize the process.

[0010] For example, assume that when the process condition is keptunchanged, a machining dimension of the particular wafer is excessiveand a machining dimension of the subsequent wafer is appropriate. Inthis case, if the process condition is controlled to reduce themachining dimension of the subsequent wafer on the basis of themonitored result that the machining dimension of the particular wafer isexcessive, the machining dimension of the subsequent wafer, which wouldinherently lead to a normal machining result, becomes less than a normalvalue. That is, in a processing apparatus in which the process result(machining result) varies with a lapse of time when the process isrepeatedly conducted, the optimal process result cannot be obtained inthe method in which a process state of a particular wafer is monitoredand variation in a process result is predicted according to data thusmonitored to control a process condition of a subsequent wafer.

[0011] Description will now be given of an apparatus and a method forprocessing a semiconductor capable of solving the problems of the priorart.

[0012] Description will be given of an apparatus and a method forprocessing a semiconductor capable of obtaining a process result at afixed level even when a process result of the processing apparatusvaries with a lapse of time.

[0013] According to the present invention, there is provided anapparatus for processing a semiconductor in which a plurality of samplesof a lot are successively supplied to a process chamber and aresuccessively processed in an intra-lot successive process. The apparatusincludes a process chamber, a sample stand for holding a sample in theprocess chamber, a process gas supply unit for supplying a process gasto the process chamber, a state sensor for detecting a state in theprocess chamber, and an intra-lot variation pattern prediction unit forpredicting, according to sensor data detected by the state sensor,intra-lot variation patterns of results of the intra-lot successiveprocess. According to a result of the prediction by the intra-lotvariation pattern prediction unit, the apparatus changes a processcondition applied to a sample of the lot.

[0014] Other objects, features and advantages of the invention willbecome apparent from the following description of the embodiments of theinvention taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0015]FIG. 1 is a diagram to explain an embodiment of a semiconductorprocessing apparatus according to the present invention.

[0016]FIG. 2 is a flowchart to explain intra-lot successive processingin a semiconductor processing apparatus.

[0017]FIG. 3 is a graph showing distributions of a machining dimensionof wafers.

[0018]FIG. 4 is a graph showing light emission spectra obtained by a lotpre-stabilizing process.

[0019]FIG. 5 is a diagram to explain a process condition setting unit.

[0020]FIG. 6 is a diagram to explain a process in the process conditionsetting unit.

[0021]FIG. 7 is a diagram to explain another embodiment.

DESCRIPTION OF THE EMBODIMENTS

[0022] Next, description will be given of an embodiment of the presentinvention by referring to the accompanying drawings. FIG. 1 is a diagramto explain an embodiment of a semiconductor processing apparatusaccording to the present invention. In conjunction with FIG. 1,description will be given of a plasma etching apparatus as an example ofthe semiconductor processing apparatus. In FIG. 1, a numeral 8 indicatesa process controller to control the entire apparatus. The controller 8receives an optimal process condition from a process condition settingunit, which will be described later, and controls respective constituentcomponents of the apparatus according to the received process condition.A numeral 10 is a magnetron to generate a microwave, a numeral 12 is awaveguide to transmit the microwave generated by the magnetron 10 to aprocess chamber 20, a numeral 14 is an impedance matching unit disposedbetween the waveguide 12 and the process chamber 20, a numeral 16 is asample stand to hold a sample 18 in the process chamber 20, a numeral 18is a sample such as a wafer, and a numeral 20 is a process chamber togenerate plasma therein.

[0023] A numeral 26 is a state sensor to detect an internal state of theprocess chamber 20, and is possibly, for example, a spectrometer tosense light emission of plasma. A numeral 28 is an optical fiber to feedthe light emitted from the plasma via a window 30 to the state sensor26, the window 30 being used to externally observe the light emission ofplasma. A numeral 32 is an exhaust pipe to discharge process gas fromthe process chamber (20), a numeral 34 is a turbo-molecular pump todischarge process gas, and a numeral 36 is a process condition settingunit which receives a spectrum of plasma light emission from, forexample, a spectrometer constituting the state sensor 26 to predict avariation pattern of a wafer machining contour in a lot according to adistribution of the spectrum to obtain an optimal process condition foreach wafer of the lot according to the variation pattern.

[0024] A numeral 38 is a gas supply pipe to feed process gas to theprocess chamber 20, a numeral 40 is a mass flow controller to controlflow rates of process gases of various types, a numeral 42 is a samplegate which opens to transport a sample 18, a numeral 44 is a load lockchamber, a numeral 46 is a sample transport arm to transport a sample,and a numeral 48 is a lot holding cassette to store, for example, a lotof samples. In this connection, the semiconductor processing apparatusdesirably includes a plurality of lot holding cassettes. A numeral 50 isa pressure controller to control gas pressure in the process chamber(20).

[0025] The process gas and the like in the process chamber 20 aredischarged by the turbo-molecular pump 34 to keep a low pressure in theprocess chamber 20. In this state, when a microwave is fed to theprocess chamber 20, plasma of the process gas is generated therein. Theplasma is used to machine a semiconductor device on a surface of thesample 18. In some cases, a high-frequency bias voltage is applied tothe sample stand 16 to move or to draw the plasma toward the sample 18.

[0026] In the description, a semiconductor wafer is used as an exampleof the sample 18. However, in addition to a semiconductor wafer, finestructures such as a liquid-crystal display panel and amicro-electro-mechanical system (MEMS) can also be used as objects ofthe machining. Additionally, a semiconductor processing apparatus usingmicrowave plasma is employed as an example of the semiconductorprocessing apparatus in the description. However, another semiconductorprocessing apparatus such as a parallel plate radio frequency (RF)plasma processing apparatus, an ICP plasma processing apparatus, or ahelicon plasma processing apparatus can be used. Moreover, aspectrometer to detect a plasma state is used as an example of the statesensor to detect an internal state of the process chamber in thedescription. However, as the state sensor, it is also possible to useanother sensor, for example, an impedance monitor or a plasma probe tomeasure electric characteristics of plasma such as a voltage and acurrent supplied to a plasma generator, phase differences of the voltageand the current, and RF components of the voltage and the current; or aninfrared or luminescence thermometer. To correctly determine theinternal state of the processing apparatus, it is desirable to obtainpossibly many kinds and possibly much monitored data by various types ofsensors.

[0027]FIG. 2 is a diagram to explain an intra-lot successive process ofthe semiconductor processing apparatus. Ordinarily, the apparatus has afunction to successively process all or part of wafers stored in a lotholding cassette 48 to improve throughput of the apparatus. Thissuccessive process is called an intra-lot successive process.

[0028] Before the intra-lot successive process, a lot pre-stabilizingprocess is conducted to adjust the state of the wall of the plasmachamber to obtain a stable machining contour. The lot pre-stabilizingprocess includes a process step called “cleaning” to remove a layer orfilm of a substance piled on the inner wall of the plasma chamber 20 ora process step called “seasoning” to modify the wall state of the plasmachamber (20; step S1).

[0029] Next, a first wafer of the lot is processed. After the firstwafer is processed, a product resultant from the wafer process piles onthe inner wall of the plasma chamber 20. Therefore, to remove the piledlayer, a wafer cleaning step is conducted for each wafer process (stepsS2 to S7).

[0030] The lot pre-stabilizing process (step S1) may be carried outunder the same process condition as for the wafer cleaning process (stepS3). The intra-lot successive process is ordinarily conducted only for agroup of wafers constituting devices which are the same to each other.

[0031]FIG. 3 is a diagram showing distributions of a machining dimensionof wafers.

[0032] As described above, the process chamber 20 is stabilized beforethe intra-lot successive processing. Next, using the process chamber, aplurality of wafers are successively processed to manufacture deviceswhich are the same to each other. Resultantly, the machining results ofthe wafers of the lot do not vary in a random way but have particularvariation patterns.

[0033] The variation patterns will be described for an example ofetching of gate electrodes of a complementary metal-oxide semiconductor(CMOS) transistor. First, using a mixture of HBr, Cl₂, and O₂ as aprocess gas, a wafer with a gate machining mask pattern copied thereonis processed by plasma etching. Width of each gate electrode afterplasma etching is a most important item of management to determineperformance of the device and is set as a machining dimension to bemonitored. FIG. 3 shows intra-lot variation patterns of the machiningcontour obtained as a result of a process by a plasma etching apparatus.Incidentally, one lot includes 25 wafers in this example.

[0034] In FIG. 3, variation pattern (A) is a variation pattern obtainedimmediately after the plasma etching apparatus is wet cleaned. Variationpattern (B) is a variation pattern obtained at a point of time whenabout 500 wafers are processed after the wet cleaning. Variation pattern(C) is a variation pattern obtained at a point of time when about 5000wafers are processed immediately before next wet cleaning.

[0035] In this way, when the wafer process is repeatedly conducted afterthe wet cleaning, the intra-lot variation pattern varies from thepattern (A) to the pattern (B) and then from the pattern (B) to thepattern (C). The progress of transition is not monotonous in proportionto the number of wafers thus processed but changes according to variousfactors such as a type of the device to be manufactured and an intervalof time between the lots. For example, in a case in which the waferprocess is not conducted at all for some reasons of the production lineand the plasma chamber is kept unused for a long period of time after avacuum exhaust process thereof, a reverse transition from the variationpattern (B) to the variation pattern (C) takes place in some cases.Therefore, the variation pattern cannot be simply predicted according tothe number of wafers processed after the wet cleaning.

[0036]FIG. 4 is a graph showing light emission spectra obtained by a lotpre-stabilizing process (step S1).

[0037] According to experiments conducted by the inventors of thepresent invention, a correlation exists between waveforms of lightemission spectra obtained in the lot pre-stabilizing process andintra-lot variation patterns of the machining contour. FIG. 4 showsexamples of light emission spectral waveforms in the lot pre-stabilizingprocess which have a correlation with the intra-lot variation patternsshown in FIG. 3. Light emission spectral waveforms (A), (B), and (C)shown in FIG. 4 respectively correspond to the variation patterns (A),(B), and (C).

[0038] Consequently, a variation pattern of intra-lot machining contourcan be predicted using the light emission spectral waveforms shown inFIG. 4.

[0039] It rarely occurs that a waveform of a light emission spectrumcompletely matches the light emission spectral waveform (A), (B), or(C). Similarly, it rarely occurs that an intra-lot variation patterncompletely matches the variation pattern (A), (B), or (C). Anintermediate state occurs in many lots. Therefore, it is required tocalculate an intra-lot variation patter in an intermediate state throughinterpolation using a light emission spectral waveform in anintermediate state.

[0040] The calculation process will now be described. Sensor data suchas light emission spectral data having a plurality of data items (ofintensity of light emission) for each wavelength can be expressed usingvectors. Therefore, an intermediate state of the vectors can becalculated using a concept of distance of the vector. For example,assume that a light emission spectrum has data items for M pixels. Thelight emission spectra (A), (B), and (C) can be expressed as follows.$\begin{matrix}{s_{A} = {{\begin{pmatrix}I_{1}^{A} \\I_{2}^{A} \\I_{3}^{A} \\\vdots \\I_{M}^{A}\end{pmatrix}\quad s_{B}} = {{\begin{pmatrix}I_{1}^{B} \\I_{2}^{B} \\I_{3}^{B} \\\vdots \\I_{M}^{B}\end{pmatrix}\quad s_{C}} = \begin{pmatrix}I_{1}^{C} \\I_{2}^{C} \\I_{3}^{C} \\\vdots \\I_{M}^{C}\end{pmatrix}}}} & (1)\end{matrix}$

[0041] Each vector component I is light emission intensity for anassociated frequency. The wavelength corresponding to each element isexpressed using vectors as follows. $\begin{matrix}{\Lambda = \begin{pmatrix}\lambda_{1} \\\lambda_{2} \\\lambda_{3} \\\vdots \\\lambda_{M}\end{pmatrix}} & (2)\end{matrix}$

[0042] In the graph of light emission spectral data of FIG. 4, thewavelength λ and the light emission intensity I are shown along theabscissa and the ordinate, respectively. Moreover, distance between twolight emission spectra S_(A) and S_(B) is expressed as l_(ab), andl_(ab) is calculated, for example, as follows. $\begin{matrix}{l_{AB} = {{s_{A} \cdot s_{B}} = {\sum\limits_{k = 1}^{M}{I_{k}^{A}I_{k}^{B}}}}} & (3)\end{matrix}$

[0043] Next, the intra-lot variation pattern of machining contour isexpressed using vectors. Assume that the intra-lot variation patterns(A), (B), and (C) are represented as vectors Δ_(A), Δ_(B), and Δ_(C),respectively. The vectors Δ_(A), Δ_(B), and Δ_(C) are then expressed asfollows. $\begin{matrix}{{\Delta_{A} = {{\begin{pmatrix}\delta_{1}^{A} \\\delta_{2}^{A} \\\delta_{3}^{A} \\\delta_{5}^{A} \\\delta_{10}^{A} \\\delta_{15}^{A} \\\delta_{20}^{A} \\\delta_{25}^{A}\end{pmatrix}\quad \Delta_{S}} = {{\begin{pmatrix}\delta_{1}^{B} \\\delta_{2}^{B} \\\delta_{3}^{B} \\\delta_{5}^{B} \\\delta_{10}^{B} \\\delta_{15}^{B} \\\delta_{20}^{B} \\\delta_{25}^{B}\end{pmatrix}\quad \Delta_{C}} = \begin{pmatrix}\delta_{1}^{C} \\\delta_{2}^{C} \\\delta_{3}^{C} \\\delta_{5}^{C} \\\delta_{10}^{C} \\\delta_{15}^{C} \\\delta_{20}^{C} \\\delta_{25}^{C}\end{pmatrix}}}}\quad} & (4)\end{matrix}$

[0044] In expression (4), a symbol δ_(i) ^(j) is a quantity of machiningdimension variation of an i-th wafer of a lot for an intra-lot variationpattern (j). As can be seen from expression (4) and FIG. 3, it is notrequired that the vector of the intra-lot variation pattern covers allwafers of the lot. The variation of the wafer not associated withexpression (4) and FIG. 3 can be calculated through interpolation usingvariations in the machining dimensions of wafers sandwiching thepertinent wafer.

[0045] For example, assume that a light emission spectrum obtainedduring the lot pre-stabilizing process in an intra-lot successiveprocess is expressed as S_(D). First, distance l_(AD) between the vectorS_(D) and the vector S_(A) representing the light emission spectrum (A),distance l_(BD) between the vector S_(D) and the vector S_(B)representing the light emission spectrum (B), and distance l_(CD)between the vector S_(D) and the vector S_(C) representing the lightemission spectrum (C) are respectively calculated.

[0046] Next, two vectors less apart from the vector S_(D) are selected.Assume that, for example, the vectors S_(A) and S_(B) are selected inthis case. For the lot, the intra-lot variation pattern can becalculated as follows. $\begin{matrix}{\Delta_{D} = \frac{{l_{BD}\Delta_{A}} + {l_{AD}\Delta_{B}}}{l_{AD} + l_{BD}}} & (5)\end{matrix}$

[0047] In the example, the calculation method uses three variationpatterns. However, even if the number of variation patterns is two ormore than three, the intra-lot variation pattern can be calculated usingexpressions (3) and (5).

[0048] The light emission spectrum shown in FIG. 4 represents meanvalues of light emission spectral data obtained during last severalseconds of the lot pre-stabilizing process. In this connection, a lightemission spectrum of mean values of the overall lot pre-stabilizingprocess can also be employed as the light emission spectrum. Also, whenthe lot pre-stabilizing process is a successive process of cleaning andseasoning, it is favorable to use a spectrum of the difference between alight emission spectrum obtained during first several seconds of theseasoning and a light emission spectrum obtained during last severalseconds of the seasoning for the following reasons. That is, the lightemission spectrum obtained during first several seconds of the seasoningis a light emission spectrum representing a state of an inner wall of aclean plasma chamber from which the piled layer is removed by theprevious cleaning. By using the difference between the light emissionspectrum and the light emission spectrum obtained during last severalseconds of the seasoning in the stabilized inner wall of the plasmachamber, a dynamic behavior of the inner wall of the chamber can bemonitored.

[0049]FIG. 5 is a diagram to explain a process condition setting unit.In FIG. 5, a numeral 54 is an intra-lot variation pattern predictionunit which receives an indication from the process controller 8 and anecessary light emission spectrum from a spectrometer during the lotpre-stabilizing process. Using the light emission spectrum from thestate sensor 26, the prediction unit 54 refers to an intra-lot variationpattern database 58 to predict an intra-lot variation pattern for a lotto be processed. According to the predicted variation pattern, theprediction unit 54 calculates a machining dimension variation for eachwafer of the lot and delivers the machining dimension variation to anintra-lot process condition setting unit 56.

[0050] A numeral 58 indicates an intra-lot variation pattern database tostore the vectors of light emission spectral waveforms S_(A), S_(B), andS_(C) and the vectors of intra-lot variation patterns Δ_(A), Δ_(B), andΔ_(C) with a correspondence established therebetween.

[0051] A numeral 56 is an intra-lot process condition setting unit whichrefers to a machining contour control recipe database 60 to calculate aquantity of correction of a process condition for each wafer to transmitan optimal process condition of each wafer to the process controller 8.A numeral 60 is a machining contour control recipe database to store aquantity of correction of the machining contour and a quantity ofcorrection of a process condition (recipe) required to obtain thequantity of correction of the machining contour.

[0052]FIG. 6 is a flowchart to explain processing of the processcondition setting unit. First, before the intra-lot successive process,the lot pre-stabilizing process is conducted to adjust the wall state ofthe plasma chamber to obtain a stable machining contour. As the lotpre-stabilizing process, the cleaning or seasoning process is performed(step S101).

[0053] Next, according to the light emission spectrum obtained from thestate sensor 2, the intra-lot variation prediction unit 54 refers to theintra-lot variation pattern database 58 6 to predict an intra-lotvariation pattern of the pertinent lot. According to the predictedvariation pattern, the prediction unit 54 then calculates a variation ofthe machining dimension for each wafer of the lot to deliver thecalculated variation to the intra-lot process condition setting unit 56(steps S102 and S103).

[0054] Subsequently, using the received variation, the condition settingunit 56 refers to the recipe database 60 to calculate a quantity ofcorrection of a process condition for each wafer and then transmits anoptimal process condition (recipe) of each wafer to the processcontroller 8 (steps S104 and S105).

[0055] Next, the process controller 8 receives the optimal processcondition and then starts the intra-lot successive process under thereceived process condition (steps S106 to S111).

[0056] According to this method, the recipe is beforehand determined foreach wafer of the lot when the intra-lot successive process is started,and hence a stable control operation can be conducted at a high speed.

[0057]FIG. 7 is a diagram to explain another embodiment. As can be seenfrom the example described above, in the calculation to obtain throughinterpolation an intra-lot variation pattern in an intermediate stateusing a light emission spectral waveform in an intermediate state, thedistance between the light emission spectra is used. However, in theembodiment, a principal component analysis is conducted to determinesimilarity of light emission spectra according to principal componentscores. In FIG. 7, a numeral 62 is a principal component analyzing unitwhich conducts a principal component analysis using the vectors of lightemission spectral waveforms S_(A), S_(B), S_(C), and S_(D) to generateprincipal component score vectors. A numeral 54′ is an intra-lotvariation pattern prediction unit which refers to the intra-lotvariation pattern database 58 according to the principal component scorevectors to predict an intra-lot variation pattern in a lot to beprocessed. According to the predicted intra-lot variation pattern, theprediction unit 54′ calculates a machining dimension variation of eachwafer of the lot and delivers the calculated variation to the intra-lotprocess condition setting unit 56. In this connection, the sameconstituent components as those shown in FIG. 5 are assigned with thesame reference numerals, and description thereof will be avoided.

[0058] First, the process controller 8 indicates to the principalcomponent analyzing unit 62 timing to acquire a light emission spectrumduring the lot pre-stabilizing process from the state sensor 26. Theanalyzing unit 62 conducts a principal component analysis according to avector S_(D) of a light emission spectral waveform received from thestate sensor and the vectors S_(A), S_(B), and S_(C) of light emissionspectral waveforms obtained from the intra-lot variation patterndatabase to generate principal component score vectors. Assume that thegenerated vectors are P_(n) ^(A), P_(n) ^(B), P_(n) ^(C), and P_(n)^(D). The intra-lot variation pattern can be predicted using aparticular number of principal component scores. However, in this case,description will be given of a method to use first and second principalcomponents. Therefore, the principal score vectors are two-dimensionalvectors.

[0059] The principal component analyzing unit 62 then sends theprincipal score vectors to the prediction unit 54. Using expression (3)defining the distance between vectors, the prediction unit 54 calculatesdistance L_(AD) between the principal component score vectors P_(n) ^(D)and P_(n) ^(A), distance L_(BD) between the principal component scorevectors P_(n) ^(D) and P_(n) ^(B), and distance L_(CD) between theprincipal component score vectors P_(n) ^(D) and P_(n) ^(C). Next, usingthe calculated vectors L_(AD), L_(BD), and L_(CD), the prediction unit54 selects two vectors closest to the vector P_(n) ^(D). Assume thatP_(n) ^(A) and P_(n) ^(B) are selected. Then, an intra-lot variationpattern ΔD can be calculated as follows. $\begin{matrix}{\Delta_{D} = \frac{{L_{BD}\Delta_{A}} + {L_{AD}\Delta_{B}}}{L_{AD} + L_{BD}}} & (6)\end{matrix}$

[0060] According to the intra-lot variation pattern ΔD, the predictionunit 54 calculates the machining dimension variation for each wafer ofthe lot and passes the calculated variation to the intra-lot processcondition setting unit 56. The unit 56 refers to the recipe database 60to calculate a quantity of correction of the process condition for eachwafer and transmits an optimal process condition (recipe) of each waferto the process controller 8. Having received the optimal recipe, theprocess controller 8 conducts the successive process for the lot.

[0061] In this way, the embodiment does not use the light emissionspectra themselves but the principal component score vectors with alimited dimension. Therefore, the intra-lot variation pattern can bepredicted without any adverse influence of disturbance of, for example,noise.

[0062] As described above, each of the embodiments of the presentinvention uses a fact that a correlation exists between a light emissionspectral waveform obtained in the lot pre-stabilizing process and anintra-lot variation pattern of the machining dimension. Therefore, theembodiment can predict the variation pattern of the machining result ina lot before the intra-lot successive process. Also, according to aresult from the prediction, the process condition can be optimized toobtain the machining result in a lot at a fixed level. That is, theoptimal recipe can be determined for each wafer of the lot when theintra-lot successive process is started. Therefore, the intra-lotsuccessive process can be controlled in a stable state and at a highspeed. In the description of the method of the embodiment, a lightemission spectrum is related to an intra-lot variation pattern usingdistance between vectors. However, various mathematical methods are alsoavailable for this purpose, and any one thereof may be used.

[0063] According to the present invention as described above, there areprovided an apparatus and a method for processing a semiconductorcapable of obtaining a process result at a fixed level even when theprocess result of the processing apparatus varies with a lapse of time.

[0064] It should be further understood by those skilled in the art thatalthough the foregoing description has been made on embodiments of theinvention, the invention is not limited thereto and various changes andmodifications may be made without departing from the spirit of theinvention and the scope of the appended claims.

What is claimed is:
 1. An apparatus for processing a semiconductor in which a plurality of samples of a lot are successively supplied to a process chamber and are successively processed in an intra-lot successive process, comprising: a process chamber; a sample stand for holding a sample in the process chamber; a process gas supply unit for supplying a process gas to the process chamber; a state sensor for detecting a state in the process chamber; an intra-lot variation pattern prediction unit for predicting, according to sensor data detected by the state sensor, intra-lot variation patterns of results of the intra-lot successive process; and a process condition change unit for changing, according to a result of the prediction by the intra-lot variation pattern prediction unit, a process condition applied to a sample of the lot.
 2. An apparatus for processing a semiconductor according to claim 1, wherein the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber.
 3. An apparatus for processing a semiconductor according to claim 1, wherein the intra-lot variation pattern prediction unit includes an intra-lot variation pattern database having stored relational information of the light emission spectral data of plasma in the process chamber detected by the state sensor and variation patterns of a machining dimension in a lot during the intra-lot successive process and refers through interpolation to the intra-lot variation pattern database according to the sensor data of the state sensor to predict intra-lot variation patterns of results of the intra-lot successive process.
 4. An apparatus for processing a semiconductor in which a plurality of samples of a lot are successively supplied to a process chamber and are successively processed in an intra-lot successive process, comprising: a process chamber; a sample stand for holding a sample in the process chamber; a process gas supply unit for supplying a process gas to the process chamber; a state sensor for detecting a state in the process chamber; and an intra-lot variation pattern prediction unit including an intra-lot variation pattern database having stored relational information of sensor data detected by the state sensor and intra-lot variation patterns of results of the intra-lot successive process, wherein the intra-lot variation pattern prediction unit refers to the intra-lot variation pattern database according to the sensor data of the state sensor to predict intra-lot variation patterns of results of the intra-lot successive process and changes, according to results of the prediction, a process condition applied to a sample of the lot.
 5. An apparatus for processing a semiconductor according to claim 4, wherein the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber.
 6. An apparatus for processing a semiconductor according to claim 4, wherein: the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber; and the intra-lot variation pattern database stores therein variation patterns of a machining dimension in a lot.
 7. An apparatus for processing a semiconductor according to claim 4, wherein the intra-lot variation pattern prediction unit includes an intra-lot variation pattern database having stored relational information of the light emission spectral data of plasma in the process chamber detected by the state sensor and variation patterns of a machining dimension in a lot during the intra-lot successive process and refers through interpolation to the intra-lot variation pattern database according to the sensor data of the state sensor to predict intra-lot variation patterns of results of the intra-lot successive process.
 8. An apparatus for processing a semiconductor in which a plurality of samples of a lot are successively supplied to a process chamber and are successively processed in an intra-lot successive process, comprising: a process chamber; a sample stand for holding a sample in the process chamber; a process gas supply unit for supplying a process gas to the process chamber; a state sensor for detecting a state in the process chamber; an intra-lot variation pattern prediction unit including an intra-lot variation pattern database having stored relational information of sensor data detected by the state sensor and intra-lot variation patterns of results of the intra-lot successive process, the intra-lot variation pattern prediction unit referring to the intra-lot variation pattern database according to the sensor data of the state sensor and thereby predicting intra-lot variation patterns of results of the intra-lot successive process; and an intra-lot process condition setting unit for calculating a process condition applied to a sample of a lot according to the intra-lot variation patterns predicted by the intra-lot variation pattern prediction unit.
 9. An apparatus for processing a semiconductor according to claim 8, wherein the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber.
 10. An apparatus for processing a semiconductor according to claim 8, wherein: the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber; and the intra-lot variation pattern database stores therein variation patterns of a machining dimension in a lot.
 11. An apparatus for processing a semiconductor according to claim 8, wherein the intra-lot variation pattern prediction unit includes an intra-lot variation pattern database having stored relational information of the light emission spectral data of plasma in the process chamber detected by the state sensor and variation patterns of a machining dimension in a lot during the intra-lot successive process and refers through interpolation to the intra-lot variation pattern database according to the sensor data of the state sensor to predict intra-lot variation patterns of results of the intra-lot successive process.
 12. An apparatus for processing a semiconductor in which a plurality of samples of a lot are successively supplied to a process chamber and are successively processed in an intra-lot successive process, comprising: a process chamber; a sample stand for holding a sample in the process chamber; a process gas supply unit for supplying a process gas to the process chamber; a state sensor for detecting a state in the process chamber; an intra-lot variation pattern prediction unit including an intra-lot variation pattern database having stored relational information of sensor data detected by the state sensor and intra-lot variation patterns of results of the intra-lot successive process, the intra-lot variation pattern prediction unit referring to the intra-lot variation pattern database according to the sensor data of the state sensor and thereby predicting intra-lot variation patterns of results of the intra-lot successive process; and an intra-lot process condition setting unit including a machining contour control recipe database having stored recipes to control machining contours for calculating a process condition applied to a sample of a lot by referring to the machining contour control recipe database according to the intra-lot variation patterns predicted by the intra-lot variation pattern prediction unit.
 13. An apparatus for processing a semiconductor according to claim 12, wherein the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber.
 14. An apparatus for processing a semiconductor according to claim 12, wherein: the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber; and the intra-lot variation pattern database stores therein variation patterns of a machining dimension in a lot.
 15. An apparatus for processing a semiconductor according to claim 12, wherein the intra-lot variation pattern prediction unit includes an intra-lot variation pattern database having stored relational information of the light emission spectral data of plasma in the process chamber detected by the state sensor and variation patterns of a machining dimension in a lot during the intra-lot successive process and refers through interpolation to the intra-lot variation pattern database according to the sensor data of the state sensor to predict intra-lot variation patterns of results of the intra-lot successive process.
 16. A method of processing a semiconductor, comprising the steps of: providing a process gas supply unit for supplying a process gas to a sample stand to hold a sample in a process chamber and to the process chamber; successively supplying a plurality of samples of a lot to the process chamber to conduct an intra-lot successive process; predicting, before a lot process is started and according to sensor data detected by a state sensor to detect a state in the process chamber, intra-lot variation patterns of results of the intra-lot successive process; and changing, according to a result of the prediction by the intra-lot variation pattern prediction unit, a process condition applied to a sample of the lot and conducting the lot process.
 17. A method of processing a semiconductor, according to claim 16, wherein the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber.
 18. A method of processing a semiconductor, according to claim 16, further comprising the steps of: preparing an intra-lot variation pattern database having stored relational information of light emission spectral data of plasma in the process chamber detected by the state sensor and variation patterns of a machining dimension in a lot during the intra-lot successive process; and referring through interpolation to the intra-lot variation pattern database according to the sensor data of the state sensor and thereby predicting intra-lot variation patterns of results of the intra-lot successive process.
 19. A method of processing a semiconductor, comprising the steps of: providing a process gas supply unit for supplying a process gas to a sample stand to hold a sample in a process chamber and to the process chamber; successively supplying a plurality of samples of a lot to the process chamber to conduct an intra-lot successive process; preparing an intra-lot variation pattern database having stored relational information of sensor data detected by the state sensor to detect a state in the process chamber and intra-lot variation patterns of results of the intra-lot successive process; referring, before a lot process is started, to the intra-lot variation pattern database according to the sensor data of the state sensor and thereby predicting intra-lot variation patterns of results of the intra-lot successive process; and changing for each sample, according to results of the prediction, a process condition applied to the sample of the lot and thereby conducting the process.
 20. A method of processing a semiconductor according to claim 19, wherein the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber; and the intra-lot variation pattern database stores therein variation patterns of a machining dimension in a lot.
 21. A method of processing a semiconductor according to claim 19, further comprising the steps of: preparing the intra-lot variation pattern database having stored relational information of the light emission spectral data of plasma in the process chamber detected by the state sensor and variation patterns of a machining dimension in a lot during the intra-lot successive process; and referring through interpolation to the intra-lot variation pattern database according to the sensor data of the state sensor and thereby predicting intra-lot variation patterns of results of the intra-lot successive process.
 22. A method of processing a semiconductor, comprising the steps of: providing a process chamber and a sample stand to hold a sample in the chamber; supplying by a process gas supply unit a process gas to the process chamber; successively supplying a plurality of samples of a lot to the process chamber to conduct an intra-lot successive process; preparing an intra-lot variation pattern database having stored relational information of sensor data detected by a state sensor to detect a state in the process chamber and intra-lot variation patterns of results of the intra-lot successive process; referring, before a lot process is started, to the intra-lot variation pattern database according to the sensor data of the state sensor and thereby predicting intra-lot variation patterns of results of the intra-lot successive process; calculating a quantity of variation in a machining dimension for each sample of a lot according to the variation patterns thus predicted; and changing for each sample, according to the quantity of variation in a machining dimension, a process condition applied to the sample.
 23. A method of processing a semiconductor according to claim 22, wherein the sensor data of the state sensor includes sensor data of spectra of light emission from plasma in the process chamber; and the intra-lot variation pattern database stores therein variation patterns of a machining dimension in a lot.
 24. A method of processing a semiconductor, according to claim 22, further comprising the steps of: preparing the intra-lot variation pattern database having stored relational information of the light emission spectral data of plasma in the process chamber detected by the state sensor and variation patterns of a machining dimension in a lot during the intra-lot successive process; and referring through interpolation to the intra-lot variation pattern database according to the sensor data of the state sensor and thereby predicting intra-lot variation patterns of the intra-lot successive process. 